Sample Data Analysis

Here are a few simple data analysis examples using Ergast’s data.

Note that, when downloading multiple sets of data, we’ll put a short Sys.sleep() in the loop to reduce load on their servers. Please be a courteous user of their free service and have similar pauses built into your analysis code. Please read their Terms and Conditions for more information.

We can make multiple repeat calls to the same function (with the same arguments) as the f1dataR package automatically caches responses from Ergast. You’ll see this taken advantage of in a few areas.

If you have example projects you want to share, please feel free to submit them as an issue or pull request to the f1dataR repository on Github.

Grid to Finish Position Correlation

We can look at the correlation between the starting (grid) position and the race finishing position. We’ll look at the Austrian Grand Prix from 2020 for this analysis, not because of any particular reason, but that it produced a well mixed field.

library(ggplot2)
# Load the data
results <- load_results(2020, 1) %>%
  mutate(
    grid = as.numeric(grid),
    position = as.numeric(position)
  )

ggplot(results, aes(x = position, y = grid)) +
  geom_point(color = "white") +
  stat_smooth(method = "lm") +
  theme_dark_f1(axis_marks = TRUE) +
  ggtitle("2020 Austrian Grand Prix Grid - Finish Position") +
  xlab("Finish Position") +
  ylab("Grid Position")

A plot of grid position (y axis) vs race finishing position (x axis) for the 2020 Austrian Grand Prix

Of course, this isn’t really an interesting plot for a single race. Naturally we expect that a better grid position yields a better finish position, but there’s so much variation in one race (including the effect of DNF) that it’s a very weak correlation. We can look at the whole season instead by downloading sequentially the list of results. We’ll filter the results to remove those who didn’t finish the race, and also those who didn’t start from the grid (i.e. those who started from Pit Lane, where grid = 0).

# Load the data
results <- data.frame()
for (i in seq_len(17)) {
  Sys.sleep(1)
  r <- load_results(2022, i)
  results <- dplyr::bind_rows(results, r)
}

results <- results %>%
  mutate(
    grid = as.numeric(grid),
    position = as.numeric(position)
  ) %>%
  filter(status %in% c("Finished", "+1 Lap", "+2 Laps", "+6 Laps"), grid > 0)

ggplot(results, aes(y = position, x = grid)) +
  geom_point(color = "white", alpha = 0.2) +
  stat_smooth(method = "lm") +
  theme_dark_f1(axis_marks = TRUE) +
  ggtitle("2020 F1 Season Grid - Finish Position") +
  ylab("Finish Position") +
  xlab("Grid Position")

A plot of grid position (y axis) vs race finishing position (x axis) for all 2020 Grands Prix

As expected, this produces a much stronger signal confirming our earlier hypothesis.

Driver Points Progress

Ergast contains the points for drivers’ or constructors’ championship races as of the end of every round in a season. We can pull a season’s worth of data and compare the driver pace throughout the season, looking at both position or total points accumulation. We’ll do that for 2021, which had good competition throughout the year for P1.

# Load the data
points <- data.frame()
for (rnd in seq_len(22)) {
  p <- load_standings(season = 2021, round = rnd) %>%
    mutate(round = rnd)
  points <- rbind(points, p)
  Sys.sleep(1)
}

points <- points %>%
  mutate(
    position = as.numeric(position),
    points = as.numeric(points)
  )

# Plot the Results
ggplot(points, aes(x = round, y = position, color = driver_id)) +
  geom_line() +
  geom_point(size = 1) +
  ggtitle("Driver Position", subtitle = "Through 2021 season") +
  xlab("Round #") +
  ylab("Position") +
  scale_y_reverse(breaks = seq_along(length(unique(points$position)))) +
  theme_dark_f1(axis_marks = TRUE)

Driver ranking after each Grand Prix of the 2021 season

What may be more interesting is the total accumulation of points. For that we can change up the plot just a little bit.

# Plot the Results
ggplot(points, aes(x = round, y = points, color = driver_id)) +
  geom_line() +
  geom_point(size = 1) +
  ggtitle("Driver Points", subtitle = "Through 2021 season") +
  xlab("Round #") +
  ylab("Points") +
  theme_dark_f1(axis_marks = TRUE)

Total points for each driver after each Grand Prix in the 2021 season

Both of these are a bit hard to read and use driver_id values that aren’t pretty on the plot. We can use some of the FastF1 look-up functions to improve our graphics (recalling that Kubica raced for Alfa Romeo for races 13 & 14 mid-season). We’ll first build a data.frame of all drivers and styles for the season, and join that to the points data.frame after we generate driver abbreviations from the Ergast driver_id.

driver_style <- rbind(
  get_session_drivers_and_teams(2021, round = 1),
  get_session_drivers_and_teams(2021, round = 13)
) %>%
  unique()

driver_style$linestyle <- driver_style$marker <- driver_style$color <- driver_style$abbreviation <- NA

for (i in seq_along(driver_style$name)) {
  if (driver_style$name[i] == "Robert Kubica") {
    # Manually handling Kubica
    style <- get_driver_style(driver_style$name[i], season = 2021, round = 13)
    driver_style$color[i] <- style$color
    driver_style$marker[i] <- 2
    driver_style$linestyle[i] <- "dotted"
    driver_style$abbreviation[i] <- style$abbreviation
  } else {
    style <- get_driver_style(driver_style$name[i], season = 2021, round = 1)
    driver_style$color[i] <- style$color
    driver_style$marker[i] <- style$marker
    driver_style$linestyle[i] <- style$linestyle
    driver_style$abbreviation[i] <- style$abbreviation
  }
}

color_values <- driver_style$color
names(color_values) <- driver_style$abbreviation

marker_values <- driver_style$marker
names(marker_values) <- driver_style$abbreviation

linestyle_values <- driver_style$linestyle
names(linestyle_values) <- driver_style$abbreviation

points <- dplyr::left_join(points, load_drivers(2021)[, c("driver_id", "code")], by = "driver_id")

With our data having graphical and label information added, we can remake these plots:

ggplot(points, aes(x = round, y = points, color = code, shape = code, linetype = code)) +
  geom_line() +
  geom_point() +
  scale_color_manual(name = "Driver", values = color_values, aesthetics = c("color", "fill")) +
  scale_shape_manual(name = "Driver", values = marker_values) +
  scale_linetype_manual(name = "Driver", values = linestyle_values) +
  ggtitle("Driver Points", subtitle = "Through 2021 season") +
  xlab("Round #") +
  ylab("Points") +
  theme_dark_f1(axis_marks = TRUE)

Remade points plot for each driver after each Grand Prix in the 2021 season, with better driver names and colors

Driver Lap Time Scatter Plot

We can look at a scatterplot of a driver’s laptimes throughout a race - possibly observing the effect of fuel usage, tire wear, pit stops, and race conditions. We’ll also show extracting constructor colour from the built-in data set.

# Load the laps data and select one driver (this time - Russell)
bot <- load_laps(season = 2021, round = 4) %>%
  filter(driver_id == "bottas")

# Get Grand Prix Name
racename <- load_schedule(2021) %>%
  filter(round == 4) %>%
  pull("race_name")

racename <- paste(racename, "2021")

# Plot the results
ggplot(bot, aes(x = lap, y = time_sec)) +
  geom_point(color = get_driver_color("Bottas", 2021, 4)) +
  theme_dark_f1(axis_marks = TRUE) +
  ggtitle("Bottas Lap times through the Grand Prix", subtitle = racename) +
  xlab("Lap Number") +
  ylab("Lap Time (s)")

Ergast Data Analysis

Introduction

Sample Data Analysis

Grid to Finish Position Correlation

Driver Points Progress

Driver Lap Time Scatter Plot